A Knowledge-Graph Powered Recommender System for Large-Volume Metrology in Reconfigurable Manufacturing System Design

Concurrent design for modern flexible manufacturing systems demands multifaceted expertise, with early-stage consideration of metrology capabilities emerging as a critical area of research to maximize productivity and quality. While digital transformation has advanced other aspects of manufacturing system design, metrology capability remains underexplored, particularly in dynamic, reconfigurable environments. This paper introduces a knowledge graph-powered recommender system designed to represent metrology capabilities and the underlying knowledge for instrument selection. The knowledge graph database captures metrology capability provided by three widely used large-volume metrology devices, laser trackers, laser radar, and photogrammetry systems, alongside their interfaces and measurement features. Selection queries based on product/part and process requirements were also proposed to guide system design decisions. The system’s feasibility was tested across three industrial aerostructure assembly scenarios, with results compared against choices made by system engineers. The proposed recommender system demonstrated its effectiveness, with future development focusing on integration with Product Lifecycle Management (PLM) systems and enhanced simulation and optimization capabilities.